Bottom Line:
In this algorithm, diffusion pattern of corpus callosum was used as prior information.Dice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations.Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results.

Methods: Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases.

Results: Dice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations. Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results.

Conclusions: The proposed method and similarity measure segment corpus callosum by propagating a hyper-surface inside the structure (resulting in high sensitivity), without penetrating into neighboring fiber bundles (resulting in high specificity).

Figure 1: Sensitivity of corpus callosum Witelson segments to PDDxThreshold for a normal subject. The graphs show number of True-Positives, number of False-Positives, and Dice correctness measure over a range of PDDxThreshold for Witelson segments of the Corpus Callosum.

Mentions:
We tested sensitivity of corpus callosum Witelson segments to PDDx_Threshold for a normal subject. Figures 1 and 2 show the number of True-Positives, the number of False-Positives, and the Dice correctness measure over a range of PDDx_Threshold for the Witelson segments of the Corpus Callosum of a normal subject and a tumor patient, respectively. As shown in these figures, for PDDx_Threshold values within a specific range (here 0.5 to 0.6), the Dice correctness measure is maximized. For a higher PDDx_Threshold, both the number of True-Positives and False-Positives start to decrease, however, the number of False-Positives tend to saturate by increasing the PDDx_Threshold. This occurs conversely for PDDx_Threshold values lower than a specific range, i.e., both the number of True-Positives and False-Positives start to increase, however, the number of True-Positives tends to saturate by decreasing PDDx_Threshold. As seen, there is a small difference between the optimal selection of PDDx_Threshold for the normal subject and the tumor case, suggesting that we can apply the same PDDx_Threshold for all datasets.

Figure 1: Sensitivity of corpus callosum Witelson segments to PDDxThreshold for a normal subject. The graphs show number of True-Positives, number of False-Positives, and Dice correctness measure over a range of PDDxThreshold for Witelson segments of the Corpus Callosum.

Mentions:
We tested sensitivity of corpus callosum Witelson segments to PDDx_Threshold for a normal subject. Figures 1 and 2 show the number of True-Positives, the number of False-Positives, and the Dice correctness measure over a range of PDDx_Threshold for the Witelson segments of the Corpus Callosum of a normal subject and a tumor patient, respectively. As shown in these figures, for PDDx_Threshold values within a specific range (here 0.5 to 0.6), the Dice correctness measure is maximized. For a higher PDDx_Threshold, both the number of True-Positives and False-Positives start to decrease, however, the number of False-Positives tend to saturate by increasing the PDDx_Threshold. This occurs conversely for PDDx_Threshold values lower than a specific range, i.e., both the number of True-Positives and False-Positives start to increase, however, the number of True-Positives tends to saturate by decreasing PDDx_Threshold. As seen, there is a small difference between the optimal selection of PDDx_Threshold for the normal subject and the tumor case, suggesting that we can apply the same PDDx_Threshold for all datasets.

Bottom Line:
In this algorithm, diffusion pattern of corpus callosum was used as prior information.Dice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations.Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results.

Methods: Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases.

Results: Dice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations. Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results.

Conclusions: The proposed method and similarity measure segment corpus callosum by propagating a hyper-surface inside the structure (resulting in high sensitivity), without penetrating into neighboring fiber bundles (resulting in high specificity).